This invention relates to a method of collecting and storing performance information of a hardware device that constitutes a storage network and of software that is run in the hardware device. More specifically, this invention relates to a method of collecting and storing storage network performance information that is suitable for a large-scale network composed of numerous components from which performance information is to be collected.
Storage networks (Storage Area Networks) structured such that plural host servers access integrated storage systems via a network are spreading widely as architecture for a data center that is capable of enhancing the utilization efficiency of storage which is ever increasing in scale and capable of cutting management cost. For performance monitoring and tuning of a business system in such a storage network environment (hereinafter referred to as SAN environment), it is necessary to comprehensively collect performance information on various hardware devices that constitute the network and on software programs and to recognize the relations between the hardware devices, between the software programs, or between the hardware devices and the software programs as well as their changes with time.
In an SAN environment, plural computers share a network device, a storage system, and other devices unlike conventional architecture where each business system is built, independently of other business systems, on a server to which a computer and an external storage system are directly connected. There is a possibility, in a shared part of the SAN, of interference on performance between business systems executed in the respective computers. This necessitates a comprehensive gathering of performance information, from which the relations between computers, network devices, and storage systems as well as a change with time of device performance are to be obtained.
Conventional performance management software designed for SANs meets this requirement by being constituted of an agent, which is posted in a network for each hardware device and software to be monitored for their performance, and management software (storage management software), which manages performance information of the whole network.
The agents directly communicate with their respective monitor subjects to obtain performance information whereas the management software collects and stores the performance information obtained by the agents to provide the performance information upon request from a storage network manager or the like.
As storage integration based on SAN becomes popular, a large-scale network often has a huge number of components (resources) and the relations between the resources are likely to be complicated. An increase in number and complexity of resources makes performance information to be kept by the storage management software sizable. In order to store performance information efficiently with a limited storage capacity of the storage system, the storage management software employs the following methods:
Method 1, in which collected performance information is stored only for a certain period specified by a storage manager and is deleted after the period passes.
Method 2, in which a limit is set to the storage capacity and older performance information is deleted each time the limit is exceeded.
Method 3, in which collected performance information of fine granularity is arranged into time-series data of less fine granularity, and the coarser the performance information is, the longer the preservation period is set. For instance, performance information is collected every minute for an hour (60 times in total), and the average and other statistical information of the collected data are calculated to obtain hourly performance information. The hourly performance information, which is coarser in granularity than the performance information collected every minute, is stored for a long period. The term “granularity” refers to the number of performance information samples taken per unit time, and a large sample number per unit time means a fine granularity whereas a small sample number per unit time means a coarse granularity.
An example of applying this Method 3 to network traffic data is found in U.S. Pat. No. 5,231,593 B.